Predictive model scoring to optimize test case order in real time

ABSTRACT

An approach for predictively scoring test case results in real-time. Test case results associated with a test run are received by a software testing environment. Using predictive statistical models, test case results and attribute relationships are matched against model rules and test case history. A statistical correlation and confidence parameter provide the ability to generate test case relationships for predicting the outcome of other test cases in the test run. The test case relationships are transformed into scoring results and output for the further processing.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of softwaretesting, and more particularly to identifying a sequence of test casesin a test environment.

Software testing is an investigation conducted to provide stakeholderswith information about the quality of the product or service under test.Software testing can also provide an objective, independent view of thesoftware to allow the business to appreciate and understand the risks ofsoftware implementation. Test techniques include, but are not limitedto, the process of executing a program or application with the intent offinding software bugs (errors or other defects).

SUMMARY

According to an embodiment, a method for predictively scoring test caseresults in real-time, the method comprising receiving one or more testcase results associated with a test run; determining one or more testcase relationships based on at least one of one or more predictivestatistical models and test case history, wherein the one or morepredictive statistical models are operational on the one or more testcase results; transforming the one or more test case relationships intoone or more scoring results based on a predetermined correlationcriteria; and outputting the one or more scoring results for furtherprocessing.

According to another embodiment, a computer program product forpredictively scoring test case results in real-time, the computerprogram product comprising one or more computer readable storage mediaand program instructions stored on the one or more computer readablestorage media, the program instructions comprising program instructionsto receive one or more test case results associated with a test run;program instructions to determine one or more test case relationshipsbased on at least one of one or more predictive statistical models andtest case history, wherein the one or more predictive statistical modelsare operational on the one or more test case results; programinstructions to transform the one or more test case relationships intoone or more scoring results based on a predetermined correlationcriteria; and program instructions to output the one or more scoringresults for further processing.

According to another embodiment, a computer system for predictivelyscoring test case results in real-time, the computer system comprisingone or more computer processors; one or more computer readable storagemedia; program instructions stored on the one or more computer readablestorage media for execution by at least one of the one or more computerprocessors, the program instructions comprising program instructions toreceive one or more test case results associated with a test run;program instructions to determine one or more test case relationshipsbased on at least one of one or more predictive statistical models andtest case history, wherein the one or more predictive statistical modelsare operational on the one or more test case results; programinstructions to transform the one or more test case relationships intoone or more scoring results based on a predetermined correlationcriteria; and program instructions to output the one or more scoringresults for further processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a distributed dataprocessing environment, in accordance with an embodiment of the presentinvention;

FIG. 2 depicts a flow of the steps of a real-time predictive scoringservice, in accordance with an embodiment of the present invention;

FIG. 3A-B depicts an example of sample test history used by a real-timepredictive scoring service and sample test history provided by areal-time predictive scoring service, respectively, in accordance withan embodiment of the present invention; and

FIG. 4 depicts a block diagram of components of a server and/or acomputing device, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION

Software testing often involves an experienced test designer definingtest cases, generating a prioritized pre-defined sequence of the testcases and running the prioritized predefined sequence of the test cases.Sequencing of test cases is based on various factors such as, but notlimited to, individual running time, perceived impact and/or featurecoverage. Further, the prioritization and/or sequencing of test casestypically occurs before a test run begins and remains unalteredthroughout the run.

Embodiments of the present invention, recognize an approach to utilize areal-time predictive model that both dynamically scores test results astest results are presented and re-sequences test cases in real-timebased on analytics of historical results and/or intrinsic test caserelationships. Embodiments of the present invention detail an approachto dynamically establish an optimized test case run sequencepredictively and in real-time.

Dynamically sequencing tests during a test run can produce benefitsrelated to testing efficiency. When focusing on a class of test results,such as test case failures, one benefit of these embodiments is thepredictive model searches historic test result attributes (data mining),uses statistical model rules to find a best fit trend from test caseresult attributes and predicts the next test case likely to fail. Theprediction allows for optimized test case sequencing, assists inuncovering failures faster and reducing the required time for a testengineer to receive test run feedback. Collecting test failureinformation as quickly as possible enables rapid failure diagnostics byprogrammers and/or developers.

Embodiments of the present invention will now be described in detailwith reference to the figures.

FIG. 1 depicts a block diagram of computing test system 100, inaccordance with one embodiment of the present invention. Elements ofcomputing test system 100 comprise a real-time scoring service 106,residing in a statistical modeling engine 108, as a real-time decisionsource for test executor 102. Further, statistical modeling engine 108is operational on computing device 110. It should be noted that FIG. 1provides an illustration of one embodiment and does not imply anylimitations with regard to other environments in which differentembodiments are implemented, e.g., real-time scoring service 106 canexist as a component separate from statistical modeling engine 108 oncomputing device 110 or as component on a separate computing device (notshown) communicatively connected to statistical modeling engine 108 vianetwork 120.

In the depicted embodiment of computing test system 100, test historydatabase 104 and test executor 102 are illustrated to establish aframework in which real-time scoring service 106 operates. It should benoted that various embodiments of real-time scoring service 106 caninclude one or more associated test executors 102 and one or more testhistory databases 104. Further, real-time scoring service 106 iscontained in a statistical modeling engine 108, of which there can beone or more statistical modeling engines 108 in various embodiments ofthe present invention.

In the depicted embodiment, components in the computing test system 100are communicatively connected via a network 120. The network 120 can bea local area network (LAN), a wide area network (WAN) such as theInternet, a cellular data network, any combination thereof, or anycombination of connections and protocols that will supportcommunications between statistical modeling engine 108, real-timescoring service 106, test history database 104, and test executor 102,in accordance with embodiments of the invention. The network 120 caninclude wired, wireless, or fiber optic connections. Statisticalmodeling engine 108, real-time scoring service 106, test historydatabase 104, and test executor 102 can include additional computingdevices, servers, or other devices not shown.

In the depicted embodiment, FIG. 1 includes test history database 104for storing historic test data and related attributes. Test historydatabase 104 is read and writable by test executor 102 and readable byreal-time scoring service 106. In other embodiments, real-time scoringservice 106 can write to test history database 104.

Continuing with the embodiment, test executor 102 runs a set of tests ona range of platforms, such as, but not limited to, operating systemplatforms. In doing so, test executor 102 monitors and manages workloadon a test platform (not depicted). Test executor 102 runs a test on aplatform and records the results into test history database 104. In oneembodiment, when a test fails, test executor 102 sends information aboutthe test toward real-time scoring service 106; when a test succeeds,test executor 102 continues cycling through tests under test executor102 control. In other embodiments, test executor 102 can communicatetest results to real-time scoring service 106 regardless of the outcomeof a test case.

Further, test executor 102 manages a set of tests to run on a set ofplatforms, such as, but not limited to, operating system platforms. Indoing so, test executor 102 processes a predetermined sequence of tests.Test executor 102 will step through each test case until a run iscomplete. For each completed test case, test executor 102 can providereal-time scoring service 106 the test case results. Test executor 102results can include but are not limited to, test case ID, test run IDand test outcome. In other embodiments, a set of test cases will beincluded in a grouping termed a “bucket” and a series of “buckets”constitute a test run. Test executor 102 processes an initially definedsequence of buckets. Test executor 102 will step through each test casein a bucket until the bucket is complete and record either individualtest case results or bucket results as an aggregate to test historydatabase 104. When each test case or bucket is completed, test executor102 can provide real-time scoring service 106 with test results. Testexecutor 102 results can include but is not limited to, bucket ID, testcase ID, test run ID and test outcome. Next, test executor 102 receivesscoring results from real-time scoring service 106 which test executor102 then uses to determine the next action in a test environment.

Test history database 104 is a repository that could be written toand/or read by test executor 102. In some embodiments, additionalfunctionality (not shown) allows an administrator or other privilegeduser to store test results and/or information about previous test runs.

Real-time scoring service 106, allows an administrator or otherprivileged user to define or configure which statistical models andrules are used by real-time scoring service 106. Model and rulemaintenance of real-time scoring service 106 can vary amongimplementations and is not depicted.

FIG. 2 depicts a flowchart 200 of the steps of real-time scoring service106, utilizing predictive analytics technology. The scoring service 106will predict the likely outcome of a series of test cases, in accordancewith an embodiment of the present invention.

In step 202, real-time scoring service 106 receives input sent by testexecutor 102, the input contains test result information from acompleted test case/bucket within a test run to allow real-time scoringservice 106 to utilize statistical modeling engine 108 to retrieve thebest match of related test cases/buckets. It should be noted that testexecutor 102 results can include but are not limited to, bucket ID, testcase ID, test run ID and test outcome.

In step 204, real-time scoring service 106 uses completed test caseinformation provided by test executor 102 to query test history database104 for use in subsequent analysis. It should be noted that an initialtest run may contain no history and real-time scoring service 106modeling rules can be configured by an administrator accordingly.

In step 206, real-time scoring service 106 uses scoring rules andstatistical methods to score test cases within a test run based on inputreceived in step 202. Example predictive scoring models include but arenot limited to, a priori, regression, clustering, tree, and neuralnetwork. The complexity of data retained in test history database 104and the complexity of the data attributes analyzed can vary depending onimplementation needs of test executor 102. Example data attributesinclude, but are not limited to, test run id, test case id, result,bucket id, run time, and associated include file. Generally, real-timescoring service 106 uses an analytical model to find related test casesthat statistically share common attributes and thereby will realizesimilar testing outcome as the test associated with the input receivedin step 202.

Continuing with step 208, real-time scoring service 106 sends outputtoward test executor 102, the output contains information test executor102 uses to determine the next action to take in a test environment. Insome embodiments, real-time scoring service 106 provides all test casesin a run regardless of whether they have been completed. In otherembodiments, real-time scoring service 106 limits output to includethose test cases that have not been executed in a test run. In otherembodiments, real-time scoring service 106 output can include asingle-value next test case to run based on highest statisticalcorrelation and confidence ranking. Further, real-time scoring service106 output can include probabilistic correlation and confidence rankingfor test executor 102 use. It should be noted that the above referencedoutput embodiments are based on and limited to input requirements oftest executor 102 as real-time scoring service 106 provides output tomeet data format and input needs of test executor 102.

Embodiments of the present invention support a range of simple tocomplex inputs and outputs of the predictive model. Real-time scoringservice 106 can provide a single-value next Bucket result or an array ofBuckets result with or without statistical weights for the test executor102 to process. All input and output format variations areimplementation decisions and not constrained by the predictive analyticsembodiments of the present invention.

FIG. 3A depicts a table of sample test history 300 created by testexecutor 102 and used by real-time scoring service 106 for analysis.Test history 300 represents one aspect of test history data that can befound in test history database 104. Heading Build ID 302 is anidentifier of historic test runs. Each test run depicts test results,indicated by heading Bucket_n, where ‘n’ indicates one to many possiblebuckets. In this example there is one Build ID 302 and are 5 testbuckets 304, 306, 308, 310, 312 per test run.

FIG. 3B, table 350 represents an example of an analytical transformationperformed by real-time scoring service 106. Table 350 data representswhat real-time scoring service 106 will produce during analysis of testhistory data in table 300 by applying an a priori association model andkeyed on test buckets 304, 306, 308, 310, 312 value equal to ‘Fail’. Theanalysis results, in the column labeled “Consequent” 352, indicates apredicted Bucket failure when a Bucket of interest in column labeled“Antecedent” 354 fails. For example, if real-time scoring service 106 isprovided with BUCKET_4 (Antecedent 354) result of ‘Fail’ as input fromtest executor 102 then real-time scoring service 106 will predictBUCKET_1 (Consequent 352) will fail when BUCKET_1 testing is executed.

Further, table 350 contains columns labeled “Support %” 356 and“Confidence %” 358 which are statistical facets of the analytical modelused and indicate statistical correlation and confidence ranking of therelative associations between Buckets. For example, if real-time scoringservice 106 is provided with BUCKET_3 (Antecedent 354) result of ‘Fail’as input from test executor 102 then BUCKET_2 (Consequent 352) has ahigher probability to Fail than BUCKET_5 (Consequent 352); due to“Support %” 356 equal to 49% and “Confidence %” 358 equal to 53% versus“Support %” 356 equal to 15% and “Confidence %” 358 equal to 48%respectively. In the above example, real-time scoring service 106 isable to prioritize and/or score relationships to be used by testexecutor 102.

FIG. 4 depicts a block diagram of components of computing device 110 inaccordance with an illustrative embodiment of the present invention. Itshould be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computer system 400 includes processors 401, cache 403, memory 402,persistent storage 405, communications unit 407, input/output (I/O)interface(s) 406 and communications fabric 404. Communications fabric404 provides communications between cache 403, memory 402, persistentstorage 405, communications unit 407, and input/output (I/O)interface(s) 406. Communications fabric 404 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric404 can be implemented with one or more buses or a crossbar switch.

Memory 402 and persistent storage 405 are computer readable storagemedia. In this embodiment, memory 402 includes random access memory(RAM). In general, memory 402 can include any suitable volatile ornon-volatile computer readable storage media. Cache 403 is a fast memorythat enhances the performance of processors 401 by holding recentlyaccessed data, and data near recently accessed data, from memory 402.

Program instructions and data used to practice embodiments of thepresent invention may be stored in persistent storage 405 and in memory402 for execution by one or more of the respective processors 401 viacache 403. In an embodiment, persistent storage 405 includes a magnetichard disk drive. Alternatively, or in addition to a magnetic hard diskdrive, persistent storage 405 can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 405 may also be removable. Forexample, a removable hard drive may be used for persistent storage 405.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage405.

Communications unit 407, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 407 includes one or more network interface cards.Communications unit 407 may provide communications through the use ofeither or both physical and wireless communications links. Programinstructions and data used to practice embodiments of the presentinvention may be downloaded to persistent storage 405 throughcommunications unit 407.

I/O interface(s) 406 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface 406 may provide a connection to external devices 408 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 408 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention can be stored on such portablecomputer readable storage media and can be loaded onto persistentstorage 405 via I/O interface(s) 406. I/O interface(s) 406 also connectto display 409.

Display 409 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method for predictivelyscoring test case results in real-time, the computer-implemented methodcomprising: receiving, by one or more computer processors, one or moretest case results associated with a test run; determining, by the one ormore computer processors, one or more test case relationships based onat least one of one or more predictive statistical models or the one ormore predictive statistical models and test case history, wherein theone or more predictive statistical models are operational on the one ormore test case results; transforming, by the one or more computerprocessors, the one or more test case relationships into one or morescoring results based on a predetermined correlation criteria; andoutputting, by the one or more computer processors, the one or morescoring results for further processing, wherein the one or more scoringresults comprise multi-value prioritized output comprising a prioritizedplurality of test cases for execution and one or more of single-valueoutput comprising a test case for execution, multi-value outputcomprising a plurality of test cases and statistical correlation andconfidence ranking for execution.
 2. The computer-implemented method ofclaim 1, wherein the one or more test case results comprise attributesassociated with the one or more test case relationships.
 3. Thecomputer-implemented method of claim 1, wherein the one or more testcase relationships are partially determined by one or more attributesassociated with the test case history.
 4. The computer-implementedmethod of claim 1, wherein determining comprises data mining historictest results to statistically organize related attributes based on theone or more predictive statistical models comprising a priori,regression, clustering, tree and neural network.
 5. Thecomputer-implemented method of claim 1, wherein predictively scoringtest case results is based on predetermined association rules and theone or more predictive statistical models.
 6. The computer-implementedmethod of claim 1, wherein the scoring results further comprise singlevalue output.
 7. The computer-implemented method of claim 6, wherein thesingle-value output is a test case for execution.
 8. A computer programproduct for predictively scoring test case results in real-time, thecomputer program product comprising: one or more computer readablestorage media and program instructions stored on the one or morecomputer readable storage media, the program instructions comprising:program instructions to receive one or more test case results associatedwith a test run; program instructions to determine one or more test caserelationships based on at least one of one or more predictivestatistical models or the one or more predictive statistical models andtest case history, wherein the one or more predictive statistical modelsare operational on the one or more test case results; programinstructions to transform the one or more test case relationships intoone or more scoring results based on a predetermined correlationcriteria; and program instructions to output the one or more scoringresults for further processing, wherein the one or more scoring resultscomprise multi-value prioritized output comprising a prioritizedplurality of test cases for execution and one or more of single-valueoutput comprising a test case for execution, multi-value outputcomprising a plurality of test cases and statistical correlation andconfidence ranking for execution.
 9. The computer program product ofclaim 8, wherein the one or more test case results comprise attributesassociated with the one or more test case relationships.
 10. Thecomputer program product of claim 8, wherein the one or more test caserelationships are partially determined by one or more attributesassociated with the test case history.
 11. The computer program productof claim 8, wherein determining comprises data mining historic testresults to statistically organize related attributes based on the one ormore predictive statistical models comprising a priori, regression,clustering, tree and neural network.
 12. The computer program product ofclaim 8, wherein predictively scoring test case results is based onpredetermined association rules and the one or more predictivestatistical models.
 13. The computer program product of claim 8, whereinthe scoring results further comprise single value output.
 14. Thecomputer program product of claim 13, wherein the single-value output isa test case for execution.
 15. A computer system for predictivelyscoring test case results in real-time, the computer system comprising:one or more computer processors; one or more computer readable storagemedia; program instructions stored on the one or more computer readablestorage media for execution by at least one of the one or more computerprocessors, the program instructions comprising: program instructions toreceive one or more test case results associated with a test run;program instructions to determine one or more test case relationshipsbased on at least one of one or more predictive statistical models orthe one or more predictive statistical models and test case history,wherein the one or more predictive statistical models are operational onthe one or more test case results; program instructions to transform theone or more test case relationships into one or more scoring resultsbased on a predetermined correlation criteria; and program instructionsto output the one or more scoring results for further processing,wherein the one or more scoring results comprise multi-value prioritizedoutput comprising a prioritized plurality of test cases for executionand one or more of single-value output comprising a test case forexecution, multi-value output comprising a plurality of test cases andstatistical correlation and confidence ranking for execution.
 16. Thecomputer system of claim 15, wherein the one or more test caserelationships are partially determined by one or more attributesassociated with the test case history.
 17. The computer system of claim15, wherein determining comprises data mining historic test results tostatistically organize related attributes based on the one or morepredictive statistical models comprising a priori, regression,clustering, tree and neural network.
 18. The computer system of claim15, wherein predictively scoring test case results is based onpredetermined association rules and the one or more predictivestatistical models.
 19. The computer system of claim 15, wherein thescoring results further comprise single value output.
 20. The computersystem of claim 19, wherein the single-value output is a test case forexecution.